Migrating at Hyperscale: How Meta Revamped Its Data Ingestion Architecture

Introduction

Meta’s social graph is powered by one of the largest MySQL deployments globally, with its data ingestion system incrementally scraping petabytes of data daily. This system feeds analytics, reporting, and downstream products—from everyday decisions to machine learning training. Recently, Meta executed a massive migration to a new architecture, improving reliability and efficiency at scale. This article shares the strategies and solutions behind that successful transition.

Migrating at Hyperscale: How Meta Revamped Its Data Ingestion Architecture — Source: engineering.fb.com

The Challenge of Large-Scale Migration

As Meta’s operations grew, the legacy system—based on customer-owned pipelines—showed instability under stricter data landing time requirements. The migration was not just about moving jobs but ensuring zero disruption while handling thousands of workflows. Key challenges included maintaining data integrity, avoiding latency regressions, and managing resource utilization—all while keeping the system operational.

Ensuring a Seamless Transition

To guarantee a smooth migration, Meta established robust tracking of the migration lifecycle for every job. This included rollout and rollback controls to quickly address issues. The core of the strategy was a phased verification process, detailed in the next section.

The Migration Lifecycle

Each job followed a strict lifecycle with three success criteria before moving to the next step:

No data quality issues: The new system produced identical data as the legacy one, verified by comparing row counts and checksums for complete consistency.
No landing latency regression: The new system matched or improved the data landing time compared to the old system.
No resource utilization regression: Performance metrics remained stable or improved.

These checkpoints ensured that only fully verified jobs transitioned, minimizing risk. The lifecycle also included gradual rollout—starting with low-impact jobs—and automated rollback if any criteria failed.

Key Strategies for Success

Beyond the lifecycle, several overarching strategies made the migration feasible:

Automated verification at scale: Cross-system comparisons were automated, allowing thousands of jobs to be checked simultaneously without manual intervention.
Incremental migration: Instead of a big bang, Meta moved jobs in waves, starting with non-critical ones and gradually increasing complexity.
Parallel run capability: Both systems ran concurrently during migration, enabling quick fallback if issues arose.
Comprehensive monitoring: Dashboards tracked job health, latency, and resource usage in real time, alerting engineers to anomalies.

These strategies, combined with the lifecycle, allowed Meta to migrate 100% of workloads and fully deprecate the legacy system without data loss or downtime.

Architectural Decisions That Influenced the Migration

The new architecture moved from customer-owned pipelines to a self-managed data warehouse service. This shift simplified operations at hyperscale. Key factors included:

Scalability: The new system could handle petabyte-scale ingestion with consistent latency.
Reliability: Centralized management reduced the failure points of decentralized pipelines.
Cost efficiency: Lower overhead by eliminating per-team pipeline maintenance.

This architectural change was a prerequisite for the migration—without it, the scale of data would have overwhelmed the legacy system.

Conclusion

Meta’s migration of its data ingestion system demonstrates that large-scale system changes are achievable with careful lifecycle management, automated verification, and incremental rollout. The new architecture now powers analytics and machine learning for millions of users, setting a robust foundation for future growth.